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About

Darja Stoeva’s research lies at the intersection of social robotics, nonverbal communication, and human-robot interaction (HRI), with a particular focus on how body language shapes affective and collaborative exchanges between humans and robots. Her most cited work, “Body Language in Affective Human-Robot Interaction” (2020, 29 citations), establishes a foundational framework for understanding how affect coordinates social interaction, emphasizing the design of robots that can both express and interpret emotional cues. Stoeva’s 2024 review on body movement mirroring and synchrony (6 citations) synthesizes technical and perceptual advances in HRI, highlighting the role of mimicry in building rapport. She has also contributed practical engineering solutions, such as an analytical inverse kinematics system for the Pepper robot (2021, 3 citations), enabling pose-matching imitation for more natural interaction. Her user study on exaggerated nonverbal cues (2022, 2 citations) reveals how amplified gestures in storytelling scenarios can alter human perception of robot personality and engagement. Together, Stoeva’s work bridges computational modeling and empirical user studies, advancing the design of socially intelligent robots that communicate through movement. Her research is particularly valuable for students and engineers interested in affective computing, embodied interaction, and the subtle dynamics of human-robot synchrony.